SimRisk: An Integrated Open-Source Tool for Agent-Based ...
SimRisk: An Integrated Open-Source Tool for Agent-Based ...
SimRisk: An Integrated Open-Source Tool for Agent-Based ...
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<strong>SimRisk</strong>: <strong>An</strong> <strong>Integrated</strong> <strong>Open</strong>-<strong>Source</strong> <strong>Tool</strong> <strong>for</strong> <strong>Agent</strong>-<strong>Based</strong><br />
Modeling, Parallel Simulation, and Formal <strong>An</strong>alysis and<br />
Optimization of Large-Scale Supply Chains under Uncertainty<br />
Principle Investigators:<br />
Li Tan<br />
School of Electrical Engineering and Computer Science, Washington State University<br />
Shenghan Xu<br />
College of Business and Economics, University of Idaho<br />
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Project Summary<br />
This grant provides funding <strong>for</strong> developing novel methodologies and an open-source tool <strong>for</strong><br />
modeling, simulating, and <strong>for</strong>mally analyzing and optimizing large-scale supply chains under uncertainty.<br />
With increasing integration of the world’s economy, the size and complexity of global<br />
supply chains are rising rapidly, so is the reliance of our economy on these supply chains. Understanding<br />
and optimizing stochastic behaviors of these large-scale global supply chains is crucial <strong>for</strong><br />
predicating and managing their uncertainty and risks. To attack computational challenges arising<br />
from the scale and complexity of these global supply chains, this interdisciplinary research will seek<br />
synergetic breakthroughs in modeling, analyzing, optimization, and implementation. Specific topics<br />
will include: 1) developing an agent-based stochastic modeling framework, which will model the<br />
elements of a supply chain as autonomous Markov decision processes. The agent-based framework<br />
will facilitate the study of complex stochastic behaviors arising from interactions of these elements;<br />
2) developing a parallel simulation technology using generative software engineering methods; 3)<br />
developing an efficient and rigorous automated <strong>for</strong>mal stochastic analysis and optimization technique,<br />
which is based on probabilistic model checking techniques developed in computer science; 4)<br />
developing an open-source tool to disseminate the technologies obtained in this project. Preliminary<br />
study and tools development have shown the benefits of this interdisciplinary research.<br />
Intellectual Merits If successful, this project will significantly improve existing supply-chain<br />
stochastic analysis and optimization technologies and tools in terms of efficiency, scalability, accuracy,<br />
and usability. Specifically, the agent-based stochastic modeling framework will extend<br />
agent-based modeling with the ability of modeling stochastic behaviors of a supply chain. To improve<br />
efficiency and scalability, the generative simulation technology will harness computational<br />
power brought by recent advances on multi-core hardware and Peta-level computing plat<strong>for</strong>ms.<br />
The <strong>for</strong>mal analysis and optimization technique will preserve the accuracy of deductive proof but<br />
will greatly improve its efficiency. The project will continue the interdisciplinary study started in<br />
[Tan and Xu, 2008, 2009a], and extends probabilistic model checking technology to supply-chain<br />
risk analysis. The project will produce an open-source tool as technology delivery plat<strong>for</strong>m, which<br />
will empower practitioners to conduct intensive analysis of risks and other stochastic factors <strong>for</strong> a<br />
global supply chain with the scale and the complexity far beyond the current technologies allow.<br />
Broader Impact Technologies developed in the project will be published in leading journals and<br />
major conferences in operations management and computer science. The majority of this grant will<br />
be used to support a junior women faculty member and a Ph.D. student. Part of this research will<br />
be built into the current and future courses on supply-chain management, mathematical modeling,<br />
software engineering, and parallel computing in Washington State University and the University of<br />
Idaho. Situated in two geographically adjacent rural areas, both universities have a long tradition<br />
of supporting women students and students from underrepresented groups in their business and<br />
engineering programs. Last but not least, the open-source tool will be freely available. Lecturers<br />
may use it to help students develop intuition behind supply-chain risk management and try “whatif”<br />
scenarios <strong>for</strong> assessing different risk management strategies. Practitioners can use it, <strong>for</strong> instance,<br />
to analyze risks arising from interactions between different elements of a global supply chain and<br />
identify deficiency in risk management plans. The analysis results will help companies improve the<br />
security of their supply chains, and in a sense, strengthen the security of our economy as a whole.<br />
1
1 Overview and objectives<br />
Project Description<br />
We propose to develop Simrisk, an integrated open-source tool <strong>for</strong> modeling, simulating, analyzing,<br />
and optimizing stochastic behaviors of large-scale supply chains under uncertainty. With<br />
increasing integration of the world’s economy, the size and the complexity of global supply chains<br />
are also rising rapidly, and so is the reliance of our economy on these supply chains. Understanding<br />
and optimizing stochastic behaviors of these large-scale global supply chains are essential <strong>for</strong> a<br />
variety of topics in supply-chain management, including risk management[Chen and Zhang, 2008],<br />
contracting [van Delft and Vial, 2004], and per<strong>for</strong>mance evaluation [Wei et al., 2007]. The existing<br />
stochastic analysis methods <strong>for</strong> supply chains (cf. [Wu et al., 2006, Finch, 2004]) include stochastic<br />
simulation (cf. [Smith et al., 2005]), deductive proof (cf. [Agrell et al., 2004]), and stochastic programming<br />
(cf. [Santoso et al., 2005]). With the first method, stochastic analysis is carried out by<br />
simulating a stochastic model under different sceneries and then using statistic methods to analyze<br />
a large set of simulation results. A drawback of this method is that even with a large number<br />
of simulation runs, analysis result is generally inconclusive because of statistic errors. To achieve<br />
desired confidence, stochastic simulation often needs to be run <strong>for</strong> many different sceneries and<br />
hence slows down the analysis process. With the second method, a typical workflow is to build an<br />
(abstract) stochastic model of a supply chain and then to analyze the model by manually proving<br />
its stochastic properties. This process often requires sophisticated mathematical skills and even<br />
with such skills, one has to simplify a supply-chain model to make it <strong>for</strong> deductive proof. Clearly<br />
it is not scalable to handle the size and complexity of today’s global supply chains, which may<br />
consists of thousands of nodes[Wu et al., 2006, Finch, 2004]; with the third method, the problem of<br />
optimizing the design of a supply-chain design is modeled as a mathematical programming problem.<br />
Although stochastic programming method can be automated, its scalability and efficiency do<br />
not meet the demand of analyzing large-scale supply chains due to the restriction of underlying<br />
optimization solvers [Santoso et al., 2005]. With the size of today’s global supply chains and<br />
recent alarming cases on risks in global supply chains, there are acute demands [Wu et al., 2006,<br />
Finch, 2004] <strong>for</strong> stochastic analysis risk analysis approach that can be scalable, fast, accurate, and<br />
easy-to-use. Such demands can not be met by existing technologies. In addition, recent advances<br />
on multicore processors and Peta-level high per<strong>for</strong>mance computing plat<strong>for</strong>m provides additional<br />
parallel computing powers on computers ranging from PCs to super computers. A research question<br />
is how to harness these parallel computing powers to improve the efficiency and scalability of<br />
existing analysis and optimization techniques <strong>for</strong> supply chains under uncertainty.<br />
The goal of project is to greatly improve scalability, efficiency, accuracy, and usability of stochastic<br />
supply-chain analysis and optimization technology. The technology and the tool developed<br />
in this project can be used in a range of applications concerning stochastic analysis of large-scale<br />
supply chains, <strong>for</strong> instance, supply-chain risk analysis and per<strong>for</strong>mance evaluation. To achieve<br />
the goal of this research, we will deploy an interdisciplinary approach to develop and integrate the<br />
following methodologies and techniques:<br />
1. <strong>An</strong> agent-based stochastic modeling framework, which models elements of a supply chain as<br />
autonomous Markov decision processes and <strong>for</strong>mally defines operational semantics <strong>for</strong> these<br />
elements and their interactions. <strong>Agent</strong>-based modeling (cf. [Axelrod, 1997]) was developed<br />
to study complexity system behaviors arising from interactions of autonomous agents. One<br />
1
enefit of agent-based modeling is that it scales well <strong>for</strong> systems with large numbers of autonomous<br />
elements (agents). Recently it draws much research interest in simulating complex<br />
social systems[Bonabeau, 2002]. <strong>An</strong>alyzing complex stochastic supply chains falls into the<br />
type of problems that an agent-based approach is prescribed <strong>for</strong>: although the stochastic behavior<br />
of each element is relatively easy to understand, uncertainty arising from interactions<br />
among these elements is not. Although there are some prior works on agent-based approach<br />
<strong>for</strong> supply chains with deterministic behaviors [Swaminathan et al., 1998], little research has<br />
been done on addressing stochastic aspect of supply chains using agent-based approach. Since<br />
understanding stochastic behaviors of supply chains is crucial <strong>for</strong> supply-chain risk analysis,<br />
an objective of this project is to fill this gap by studying agent-based modeling <strong>for</strong> supply<br />
chains under uncertainty.<br />
2. Generative parallel simulation technology, which simulates a supply-chain model by generating<br />
executable simulation code. Recent advance in multi-core hardware and the emerge<br />
of peta-level parallel computing plat<strong>for</strong>ms provide extra parallel computing powers <strong>for</strong> computers<br />
ranging from desktops to super computers. To take full advantage of these parallel<br />
architectures, our generative simulation technology will generate executable simulation code<br />
optimized <strong>for</strong> these parallel architectures.<br />
3. Formal stochastic analysis and optimization technique, which is derived from probabilistic<br />
model checking technique developed in computer science. In contrast to stochastic simulation,<br />
the <strong>for</strong>mal stochastic analysis technique constructs a rigorous proof in a fully automated<br />
manner. It provides mathematically sound stochastic analysis <strong>for</strong> supply-chain applications<br />
more concerning the accuracy of analysis result. Developed <strong>for</strong> verifying stochastic system<br />
designs, probabilistic model checking has made significant progress in past several years with<br />
the development of efficient symbolic algorithms and open-source tools ([Hinton et al., 2006]).<br />
To the best of our knowledge, in [Tan and Xu, 2008] we are the first to introduce probabilistic<br />
model checking <strong>for</strong> supply chain risk analysis. In this project we will conduct an extensive<br />
interdisciplinary study on applying model-checking-based technology to supply-chain analysis.<br />
We will address issues including pattern-based problem <strong>for</strong>mulation, proof extraction, and<br />
result interpretation. In [Xu and Tan, 2009] we proposed a model-checking-based approach<br />
<strong>for</strong> scheduling optimization. In this project we will extend this work to optimize operations<br />
of supply chains under uncertainty.<br />
A<strong>for</strong>ementioned theoretical study lays the foundation <strong>for</strong> Simrisk, the open-source tool we will<br />
develop as a plat<strong>for</strong>m to delivery new technologies technology delivery plat<strong>for</strong>m. Specifically, we<br />
will develop an agent-based visual modeling language as Simrisk’s front end. Simrisk will include<br />
a code generator that can product simulation code in C/C++ targeted <strong>for</strong> specific parallel plat<strong>for</strong>ms.<br />
Simrisk will also have a <strong>for</strong>mal analysis and optimization module that allows a user to<br />
<strong>for</strong>mally specify stochastic properties of interest and provides a push-button approach to analyze<br />
and optimize stochastic supply chains with respect to these properties. The design of Simrisk<br />
will emphasize on extensibility. Simrisk is not only an implementation of modeling and analysis<br />
technologies developed in this project, but also an open plat<strong>for</strong>m which users may extend with<br />
new functionalities. The tool will be built on Eclipse, a leading open-source software development<br />
environment [the Eclipse Foundation, since 2004]. Eclipse is known <strong>for</strong> its extensibility: users can<br />
extend its functionalities with customized plugins. We will define an interface which allows third<br />
2
party to develop analysis plugins <strong>for</strong> Simrisk. We will also develop an object-oriented type system<br />
<strong>for</strong> supply chains, by which a practitioner defines new supply-chain elements suitable <strong>for</strong> his/her<br />
application.<br />
Our interdisciplinary team possesses expertise and skills essential <strong>for</strong> the success of the proposed<br />
research. Dr. Li Tan is on computer science faculty in Washington State University. He conducted<br />
research on model checking, model-based design and simulation, and software analysis. Dr. Shenghan<br />
Xu is on the business faculty of the University of Idaho. Her research is on supply-chain<br />
management. We already collaborated in the preliminary study of this research on the components<br />
of the proposed project and published the results. For example, in [Tan and Xu, 2009b] we developed<br />
a prototype of an agent-based supply chain modeling and simulation tool; we introduced a<br />
probabilistic-model-checking <strong>for</strong>mal stochastic analysis technique in [Tan and Xu, 2008] and used<br />
it to analyze risks in supply chain consolidation in [Tan and Xu, 2009a]. Please see Section 3.2 <strong>for</strong><br />
more details. Our team members also developed a range of open-source tools in model checking<br />
[Cleaveland et al., 2000], model-based design [Tan, 2006], and supply chain modeling and analysis<br />
[Tan and Xu, 2009b] etc.<br />
2 Expected Significance<br />
The importance of stochastic supply-chain analysis is acknowledged in a wide range of applications,<br />
including risk supply-chain risk analysis [Chen and Zhang, 2008], contracting [van Delft and Vial,<br />
2004], and per<strong>for</strong>mance evaluation [Wei et al., 2007]. Existing stochastic analysis technologies and<br />
tools do not possess efficiency, accuracy, scalability, and usability required by analyzing contemporary<br />
global supply chains [Finch, 2004, Wu et al., 2006]. To reduce cost and maintain profit margin,<br />
nowadays many companies engage themselves in global supply chain expansion involving suppliers,<br />
distributors, retailers, and logistics providers across multiple continents [Ferrer and Karlberg,<br />
2006]. For example, the Sears Holding company operates more than 3,800 full-line and specialty<br />
retail stores including Kmart and Sears stores in the United States and Canada [Sears Holding<br />
Company, 2008]. The wholesale chain Costco operates its 544 warehouse stores in North America,<br />
South America, Asia, and Europe. It sources merchandise from all over the world [Costco Wholesale<br />
Corporation, 2007]. The existing stochastic analysis technologies cannot meet the demand of<br />
analyzing large-scale supply chains in terms of scalability, efficiency, accuracy, and usability.<br />
The purpose of this project is to develop an open-source tool and its underlying technologies<br />
that are efficient and scalable <strong>for</strong> analyzing and optimizing real-world large-scale supply chains<br />
under uncertainty. Specifically we expect that this research will advance the state-of-the-art in the<br />
following technologies: agent-based supply chain modeling, generative parallel simulation technology,<br />
and <strong>for</strong>mal analysis and optimization technique. We will leverage recent advances in software<br />
engineering, computer architecture, and <strong>for</strong>mal methods, and apply them to stochastic supply-chain<br />
analysis. By doing so, this project also promotes synergy between computer science and operations<br />
management. Technology advance achieved by this project will be delivered in an open-source tool.<br />
The tool will empower practitioners to analyze risks and uncertainty arising from interactions of<br />
different elements in supply chains on much larger scale and at finer granularity. <strong>An</strong>alysis result can<br />
be used to help companies improve the design and management of their supply chains. The project<br />
will also enable sophisticated analysis on complex stochastic properties and “what-if” scenarios, it<br />
will help companies balance risk management with other operational factors, and streamline their<br />
supply-chain operations.<br />
3
3 Background<br />
3.1 Related Works<br />
Existing stochastic analysis techniques <strong>for</strong> supply chains include stochastic simulation, deductive<br />
proof, and stochastic programming. Stochastic simulation works by simulating operations of a supply<br />
chain under different sceneries and then applying statistic methods to the simulation results.<br />
Stochastic simulation is widely used <strong>for</strong> a variety of applications such as risk analysis and per<strong>for</strong>mance<br />
evaluation (cf. [Kleijnen, 2005]). Although stochastic simulation generally scales well, it is<br />
also prone to statistic errors; in deductive proof, one can prove stochastic properties of a supply<br />
chain using an array of methods, such as Bayesian networks, Fuzzy logics, and Hybrid networks<br />
(cf. [Pai et al., 2003]). Deductive proof requires a strong theoretical background. Since deductive<br />
proof is often done by hand, it is not suitable <strong>for</strong> large-scale supply chains; stochastic programming<br />
approach models the design of a supply chain with uncertainty as an optimization problem. For<br />
example, MirHassani et al. [2000] considered an integer stochastic programming (ISP) representation<br />
and solution <strong>for</strong> a two-stage design of a supply chain. The first stage concerned decisions on<br />
opening/close of facilities and their capacity levels be<strong>for</strong>e future demands were realized. The second<br />
stage involved production and distribution plans during supply-chain operation. The authors<br />
used Benders decomposition to solve stochastic programming programs <strong>for</strong> supply chain networks<br />
involving up to 8 manufacturing sites, 15 distribution centers, 30 retailers, and with 100 scenarios.<br />
Alonso-Ayuso et al. [2003] also studied two-stage stochastic supply chain using stochastic programming<br />
and proposed an algorithm based branch-and-fix heuristic. They tested their algorithm on<br />
networks up to 6 manufacturing sites, 12 products, 24 market regions, and with 23 scenarios. Santoso<br />
et al. [2005] noticed the limitation of these techniques in terms of scalability. They proposed a<br />
solution technique that leveraged the strength of statistic simulation. They developed a sample average<br />
approximation schema (SAA) to approximate expectations in the objective function and then<br />
applied an accelerated Benders decomposition problem to solve the rest of stochastic programming<br />
problem. Although their approach could handle much bigger supply chain design problems, it also<br />
suffered from the similar drawback of stochastic simulation: the accuracy of its result was prone<br />
to statistic errors. Additionally the application of stochastic programming was limited to design<br />
optimization, whereas requests <strong>for</strong> stochastic analysis may come from many other applications, <strong>for</strong><br />
instance, analyzing an existing design of a supply chain under “what-if” scenarios.<br />
In this project we will develop an agent-based modeling framework as the front-end of the<br />
analysis tool. Swaminathan et al. [1998] studied a multi-agent approach <strong>for</strong> modeling supply-chain<br />
dynamics. As with us, they believed a multi-agent modeling framework is “a natural choice”<br />
<strong>for</strong> modeling supply chains because “supply-chain management is fundamentally concerned with<br />
coherence among multiple decision makers”. They modeled structural elements as agents, which<br />
interacted with each others using control elements. Our agent-based research follows the same<br />
line but we will extend it with stochastic modeling using Markov decision processes. Additionally<br />
we will provide <strong>for</strong>mal syntax and semantics to remove ambiguity in model interpretation and to<br />
facilitate rigorous <strong>for</strong>mal analysis.<br />
Our <strong>for</strong>mal analysis technique will be based on probabilistic model checking [Bianco and de Alfaro,<br />
1995]. In recent years the scalability and efficiency of probabilistic model checking was drastically<br />
improved with the development of symbolic techniques such as Multi-Terminal Binary Decision<br />
Diagrams (MTBDD) [Wang and Kwiatkowska, 2005]. Probabilistic model checkers such as PRISM<br />
[Hinton et al., 2006] have been successfully applied to large-scale complex systems in biological<br />
4
processes [Kwiatkowska et al., 2008] and Randomised distributed algorithms [Kwiatkowska and<br />
Norman, 2002]. We will use PRISM as the underly decision procedure <strong>for</strong> <strong>for</strong>mal stochastic analysis.<br />
We will develop a pattern-based method <strong>for</strong> a user to specify stochastic properties of a supply<br />
chain. We also will extend the decision algorithm in PRISM so we can extract the proof from a<br />
model-checking run and develop a game-theoretic approach to interpret the proof to an end user.<br />
On the implementation side, Rossetti et al. [2006] used an objective-oriented framework to<br />
implement a Java package <strong>for</strong> supply-chain simulation. Liu et al. [2004] introduced a Java-based<br />
supply-chain simulation tool Easy-SC. In Easy-SC modeling environment, facilities were modeled<br />
as instances of pre-defined six enterprise nodes, and routes were implemented as connection arcs.<br />
Wang et al. [2008] discussed a general business simulation environment (GBSE) developed by IBM<br />
China research lab. GBSE was a Java-based event-driven simulation tool built on top of the<br />
Eclipse plat<strong>for</strong>m [the Eclipse Foundation, since 2004]. Our proposed open-source tool will also be<br />
developed on Eclipse <strong>for</strong> better extensibility. In contrast to GBSE’s conventional implementation<br />
of a simulator, in which the simulation logic is integrated with the tool, our tool will implement<br />
a generative simulation engine, which will generate stand-alone simulators tailored <strong>for</strong> targeted<br />
hardware architectures.<br />
3.2 Preliminary Study<br />
To demonstrate benefits and feasibility of our ideas, we have conducted a preliminary study on<br />
components of the proposed research.<br />
In [Tan and Xu, 2008] we proposed a model-checking-based <strong>for</strong>mal stochastic analysis framework<br />
<strong>for</strong> supply chains. The framework used an extension of Markov decision processes to model<br />
elements of a supply chain. We encoded stochastic properties of a supply chain in the Probabilistic<br />
Computational Tree Logic (PCTL). To the best of our knowledge, this was the first published<br />
work on applying probabilistic model checking technique to supply chain analysis. In [Tan and<br />
Xu, 2009a] we further studied the benefits of the <strong>for</strong>mal stochastic analysis by using it to analyze<br />
risks in supply chain consolidation. We conducted a quantitative analysis of the impact of four<br />
consolidation strategies on risks in a 4-echelon supply chain. Figure 1 shows the configurations of<br />
supply chain networks used in the experiments in [Tan and Xu, 2009a]. Figure 1.(a) shows two independent<br />
networks. Figure 1.(b) shows the consolidated supply chain resulting from a merger and<br />
acquisition with product pooling strategy. Figure 2 reports a result of <strong>for</strong>mal stochastic analysis. It<br />
shows the probability of on-time deliveries under different supply-chain consolidation strategies. In<br />
[Tan and Xu, 2009b] we tested an implementation of agent-based stochastic modeling approach <strong>for</strong><br />
supply chains. The implementation included an object-oriented type system <strong>for</strong> improving model<br />
reusability. Additional results from our preliminary study were also presented in [Xu and Tan,<br />
2008, Tan and Xu, 2009c].<br />
4 Relations to the principal investigators’ long-term goals<br />
The long term goal of this research is to advance modeling and automated analysis technologies<br />
<strong>for</strong> analyzing stochastic and temporal behaviors of large-scale supply chains. The proposed project<br />
represents a significant step towards this long-term goal. Our preliminary study has shown the<br />
feasibility and benefits of every technological components in this proposed research, including agentbased<br />
stochastic modeling [Tan and Xu, 2008], generative simulation technology [Tan and Xu,<br />
5
s a<br />
s b<br />
u a1 /d a1<br />
w11 a w11<br />
b u w12 a w11<br />
b b1 /d b1 u a1 /d a1 u b1 /d b1<br />
w21 a w21<br />
b u b2 /d b2<br />
u w22 a w22<br />
b u b2 /d b2<br />
w23 a w23<br />
b u b2 /d b2<br />
w24 a w24<br />
b a2 /d a2<br />
u a2 /d a2<br />
u a2 /d a2<br />
u a2 /d a2<br />
u b2 /d b2<br />
r 1 r 2 r 3 r 4 r 5 r 6 r 7 r 8 r 9 r 10 r 11 r 12 r 13 r 14 r 15 r 16<br />
(a) Two supply chains operated separately by companies A and B<br />
s a<br />
s b<br />
u a1 /d a1<br />
w11 a w11<br />
b u w12 a w11<br />
b b1 /d b1 u a1 /d a1 u b1 /d b1<br />
w21 a w21<br />
b u b2 /d b2<br />
u w22 a w22<br />
b u b2 /d b2<br />
w23 a w23<br />
b u b2 /d b2<br />
w24 a w24<br />
b a2 /d a2<br />
u a2 /d a2<br />
u a2 /d a2<br />
u a2 /d a2<br />
u b2 /d b2<br />
r 1 r 2 r 3 r 4 r 5 r 6 r 7 r 8 r 9 r 10 r 11 r 12 r 13 r 14 r 15 r 16<br />
(b) A consolidated supply chain with product pooling.<br />
Figure 1: Supply chain configurations be<strong>for</strong>e and after a merger. d qi and u qi are the failure and<br />
recovery rates of a level-i warehouse operated by q ∈ {a, b} [Tan and Xu, 2009a].<br />
L-2 Cross-Warehouse<br />
L-1 Cross-Warehouse<br />
Product pooling<br />
Improvement<br />
10 4.0<br />
10 3.0<br />
10 2.0<br />
10 1.0<br />
10 0.0<br />
10 -1.0<br />
10 3.5<br />
10 3.0<br />
10 2.5<br />
10 2.0<br />
10 1.5<br />
10 1.0<br />
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10 -0.5<br />
0<br />
0.1<br />
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0.3<br />
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0.5<br />
0.6<br />
Stability of network A<br />
0.7<br />
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0.9<br />
1<br />
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0.6<br />
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0.4 Stability of network B<br />
0.3<br />
0.2<br />
0.1<br />
1 0<br />
Figure 2: Improvement of probability of on-time delivery with different supply-chain consolidation<br />
strategies.<br />
6
2009b], and model-checking-based <strong>for</strong>mal analysis technique[Tan and Xu, 2009a]. We also developed<br />
a prototype of a modeling and simulation tool <strong>for</strong> supply-chain analysis [Tan and Xu, 2009a].<br />
These accomplishments have prepared us to take on this project. This grant will provide crucial<br />
resources we need to advance and complete the research we started in our preliminary study. If<br />
funded, this project will leverage the benefits of recent advances in computer science, especially<br />
in software engineering and <strong>for</strong>mal methods and apply them to stochastic supply-chain analysis.<br />
The project will also advance theories and methods crucial <strong>for</strong> modeling and analysis of largescale<br />
stochastic supply chains. Finally the project will produce an open-source tool Simrisk as a<br />
plat<strong>for</strong>m <strong>for</strong> delivering new analysis and optimization technologies to practitioners and researchers.<br />
Simrisk will also serve as an open plat<strong>for</strong>m and technology testbed that allows other researchers to<br />
experiment new technologies. If successful, this project will empower researchers and practitioners<br />
with technologies and an open-source tool that are scalable <strong>for</strong> analyzing large-scale global supply<br />
chains under uncertainty. With this new capability, next we will apply Simrisk to supply-chain<br />
risk management, contracting, and per<strong>for</strong>mance evaluation. As the follow-up of this project, we<br />
will team with industrial leaders such as Boeing, which we already have collaboration with on<br />
supply chain consulting, and research institutions such as Pacific Northwest National Lab (PNNL),<br />
which we are collaborating with on high-per<strong>for</strong>mance simulation technology. We will apply the<br />
methods and the tool produced in this project to the analysis of intercontinental supply chains<br />
using Peta-scale computing plat<strong>for</strong>ms.<br />
5 Research plan<br />
5.1 Specific aim 1: develop an agent-based stochastic supply-chain modeling<br />
framework<br />
The framework will model elements of a supply chain as automatous stochastic agents. It will also<br />
<strong>for</strong>mally define operational semantics of these agents and their interactions. This is the start point<br />
of this project since other activities rely on this model language as the front end. Specifically we<br />
will address the following issues,<br />
1. <strong>Agent</strong> modeling. This activity will consider how to model supply chain elements as autonomous<br />
agents. Since our emphasis is on stochastic behaviors of these elements and each<br />
element has its own decision logic, we will model agents as Markov decision processes. In [Tan<br />
and Xu, 2008] we proposed an extension of Markov decision process (EMDP) and modeled<br />
each element as a restricted two-state EMDP. For example, every element in a 4-echelon supply<br />
chain in Figure 1.(a) has only two states: working and failed. In this project, we plan to<br />
lift such restriction. We will encode more complex decision logic <strong>for</strong> each element. Moreover,<br />
we will make the following advances in agent modeling <strong>for</strong> supply chains:<br />
(a) Extend E-EMDP (Element Extended Markov Decision Process) in [Tan and Xu, 2008] to<br />
include logic that models the decision process of an element. For instance, <strong>for</strong> warehouse<br />
w11 a in Figure 1.(a), the proposed extension will also support the encoding of its ordering<br />
and distribution logic. The ordering logic of w11 a will decide when and where to place<br />
orders based on factors including w11 a ’s inventory, the pricing structures of its suppliers<br />
s a and s b , and requests from its customers w21 a and wa 22 .<br />
7
(b) Support component-based design. <strong>An</strong> important issue in model-based system design is<br />
how to break a design model to more manageable pieces. This is especially important <strong>for</strong><br />
designing and modeling large-scale supply chains. Our agent modeling will address this<br />
issue by supporting behaviorial composition and hierarchy. We will base our technique<br />
on a proven component-based design technique, which is used in StateChart [Harel,<br />
1987] and its better known variant UML state diagram [Object Management Group,<br />
since 1997]. For instance, consider the decision logic of w11 a in Figure 1.(d). Without<br />
involving too much technical details, Figure 3 shows an abstract model of the logic.<br />
It illustrates several features we will develop in this project: (1) behavior hierarchy.<br />
For example, at the top level of Figure 3 there are two states: working and failed.<br />
The logic <strong>for</strong> ordering and distribution is further defined underneath the working state.<br />
Similarly the details of the ordering decision logic is given underneath its representing<br />
state. Behavior hierarchy provides a multi-level abstraction of the decision logic of an<br />
element. A designer or a modeler can focus on part of the logic (s)he is interested in at<br />
the right level of granularity; (2) behavior composition, which is implemented as parallel<br />
compositions of sub-states. Each sub-state represents an independent unit of logic. In<br />
Figure 3 the working state of w11 a contains two parallel sub-states: one state representing<br />
ordering logic and the other representing distribution logic. Behavior composition allows<br />
a component of a decision logic to be developed and tested independently be<strong>for</strong>e being<br />
integrated with others.<br />
(c) Improve model reusability. As part of our goal <strong>for</strong> this project, we want to develop<br />
methods and a tool that can be used <strong>for</strong> modeling and analyzing practical stochastic<br />
supply chains. As a principle in software engineering, code (and model) reuse not just<br />
helps productivity by reusing existing code (and model), it also helps improve quality<br />
of code (and model) by focusing verification and validation activities on reusable components.<br />
Using type theory developed in programming languages, we will develop an<br />
object-oriented model-reuse technique <strong>for</strong> agent-based supply-chain modeling. Figure 4<br />
shows a draft of the object-oriented type hierarchy we will use to support model reuse.<br />
The type hierarchy supports model reuse by abstracting common behaviors of similar<br />
elements to a super class. For instance, class agent summarizes behaviors and attributes<br />
essential to all types of supply-chain elements. Class node is a special case of agent but it<br />
contains behaviors and attributes common to all the facility nodes, including suppliers,<br />
warehouse, and retailers.<br />
(d) Support stochastic modeling and decision optimization. In [Tan and Xu, 2008] we extended<br />
Markov decision processes to model stochastic behaviors of an element of a supply<br />
chain. For example, transitions between a working state and a failed state have optional<br />
probability labels. These labels define the failure and recovery probabilities of an element.<br />
In this project, we propose to extend our previous work with the capability of<br />
decision optimization. Specifically we will combine the rewards mechanism defined in<br />
Markov decision processes with probabilistic model checking technique. The combined<br />
approach can find the optimal decision <strong>for</strong> an element of a stochastic supply chain under<br />
a given scenario.<br />
8
Working<br />
[!AllSuppilersAvail]<br />
Ordering Decision Logic<br />
One-supplier Strategy<br />
Order Splitting Strategy<br />
[supplier=s a ]<br />
Decide supplier<br />
[supplier=s b ]<br />
Compute orders<br />
Order from s a<br />
Order from s a<br />
e clock<br />
Compute order<br />
Compute order<br />
Place order<br />
e clock<br />
e clock<br />
Place order<br />
Place order<br />
[AllSuppilersAvail]<br />
Distribution Decision Logic<br />
Check inventory<br />
e clock<br />
Compute<br />
Distribution<br />
Distribute<br />
Failed<br />
Figure 3: A component-based design <strong>for</strong> the decision logic of warehouse w a 11 .<br />
9
Figure 4: <strong>An</strong> object-oriented type hierarchy <strong>for</strong> model reuse.<br />
5.2 Specific aim 2: develop high-per<strong>for</strong>mance generative simulation technology<br />
Simulation still plays an important and indispensable role in the practice of supply chain<br />
management. For this reason, we will retain simulation as part of our integrated stochastic<br />
analysis framework but will greatly improve its per<strong>for</strong>mance. Much of existing research on<br />
supply-chain simulation focuses on simulation algorithms and software implementation (cf.<br />
[Terzi and Cavalieri, 2004, Kleijnen, 2005]). In this research, our interest on simulation is to<br />
improve its efficiency and scalability by providing a better integration between software and<br />
hardware. Our research is motivated by recent advances in multi-core architecture and Petalevel<br />
computing plat<strong>for</strong>ms. These advances provide extra computation power <strong>for</strong> computers<br />
ranging from desktops to super computers. A research question is how to harness these<br />
powers to improve the speed and scalability of supply-chain simulation. Our solution is to<br />
develop a reconfigurable generative simulation approach <strong>for</strong> supply-chain analysis. For each<br />
supply-chain model, the proposed approach will on-the-fly generate simulation code that can<br />
take advantage of a targeted computer architecture. Specifically we will develop a generative<br />
simulation engine that can be reconfigured <strong>for</strong> two different types of architectures: multicore<br />
personal computers and high-per<strong>for</strong>mance clusters. For a desktop computer with a<br />
single multi-core processor, the engine will generate a multi-thread program. Each thread<br />
represents an agent using the IEEE POSIX thread model [IEEE and the open group, 2004].<br />
For a cluster of multi-core processors, the generative simulation technology will generate a<br />
set of POSIX threads <strong>for</strong> each processor, and different sets of threads will communicate via<br />
Message Process Interface (MPI).<br />
A central issue in developing generative simulation engine is load balancing. That is, given<br />
10
Server 1<br />
Memory<br />
Server 2<br />
Memory<br />
Server N<br />
Memory<br />
L2<br />
cache<br />
C<br />
o<br />
r<br />
e<br />
1<br />
L2<br />
cache<br />
C<br />
o<br />
r<br />
e<br />
2<br />
L2<br />
cache<br />
C<br />
o<br />
r<br />
e<br />
3<br />
L2<br />
cache<br />
C<br />
o<br />
r<br />
e<br />
4<br />
L2<br />
cache<br />
C<br />
o<br />
r<br />
e<br />
1<br />
L2<br />
cache<br />
C<br />
o<br />
r<br />
e<br />
2<br />
L2<br />
cache<br />
C<br />
o<br />
r<br />
e<br />
3<br />
L2<br />
cache<br />
C<br />
o<br />
r<br />
e<br />
4<br />
…<br />
L2<br />
cache<br />
C<br />
o<br />
r<br />
e<br />
1<br />
L2<br />
cache<br />
C<br />
o<br />
r<br />
e<br />
2<br />
L2<br />
cache<br />
C<br />
o<br />
r<br />
e<br />
3<br />
L2<br />
cache<br />
C<br />
o<br />
r<br />
e<br />
4<br />
PCI-e<br />
PCI-e<br />
PCI-e<br />
Fiber Optical Channel<br />
Figure 5: A cluster of quad-core servers.<br />
a hardware architecture and an agent-based model, how to distribute threads and processes<br />
to cores and processors <strong>for</strong> better per<strong>for</strong>mance. This issue has a special meaning in a cluster<br />
environment: a multi-core cluster supports both shared-memory and message-passing communication,<br />
which have very different characteristics. We will study optimal distribution<br />
of threads and processes <strong>for</strong> minimizing communication overhead in context of agent-based<br />
supply-chain simulation. Specifically we will study the following methods:<br />
(a) Explore model structure and data dependency to improve load balancing. For example,<br />
consider the supply chain in Figure 1.(b), and assume that we will run simulation on<br />
a cluster of quad-core processors, whose architecture is shown in Figure 5. Figure 6<br />
shows a distribution of threads and processes using heuristics from model structure<br />
and data dependency. The communication between supply-chain elements is through<br />
shipments and messages. We assume that messages can only be passed along routes.<br />
As a general principle, threads <strong>for</strong> a sub-network of closely coupled elements will be<br />
placed on the cores of the same processor. These closely coupled elements require more<br />
frequent communication between them, which shall be implemented with less overhead<br />
using shared memory. As an example, in Figure 6 threads <strong>for</strong> the elements of the<br />
sub-networks of w21 a and wb 21 are assigned to the same processor, and processes <strong>for</strong> the<br />
sub-networks of s a and s b are allocated to different processors. In general, the higher<br />
elements are in a network hierarchy, the less they will communicate with each other, since<br />
the operations of elements on a higher level will be planned over a much bigger planning<br />
horizon. We reserve shared memory <strong>for</strong> communication among closely coupled low-level<br />
elements and use message passing <strong>for</strong> communication among high-level elements.<br />
(b) Profile threads and optimize thread scheduling. To further improve the per<strong>for</strong>mance<br />
of generated parallel simulators, we will profile the execution time and the overhead of<br />
threads, and use this in<strong>for</strong>mation to optimize thread scheduling. Using the result from<br />
profiling, the generative simulation engine will express the thread scheduling problem as<br />
a linear programming problem. It will use the optimization result to define scheduling<br />
policy <strong>for</strong> threads.<br />
11
Server 1/Core 3<br />
Server 2/Core 3<br />
s a<br />
s b<br />
u a1 /d a1<br />
w11 a w11<br />
b u w12 a w11<br />
b b1 /d b1 u a1 /d a1 u b1 /d b1<br />
u a2 /d a2 u b2 /d b2 u a2 /d a2 u b2 /d b2 u a2 /d a2 u b2 /d b2 u a2 /d a2 u b2 /d b2<br />
w a 21 w b 21<br />
w a 22 w b 22<br />
w a 23 w b 23<br />
w a 24 w b 24<br />
r 1 r 2 r 3 r 4 r 5 r 6 r 7 r 8 r 9 r 10 r 11 r 12 r 13 r 14 r 15 r 16<br />
Server 1/Core 1 Server 1/Core 2 Server 2/Core 1 Server 2/Core 2<br />
Figure 6: A distribution of threads <strong>for</strong> simulating the model in Figure 1.(b) on the cluster in Figure<br />
5.<br />
5.3 Specific aim 3: develop efficient <strong>for</strong>mal stochastic analysis technique <strong>for</strong><br />
supply chains<br />
Model checking [Clarke et al., 1986] is an automated verification technique in which the specification<br />
of a system is <strong>for</strong>mally encoded in a temporal logic. Model checking provides a “push-button”<br />
approach to algorithmically verify a system on a temporal property and produce a mathematically<br />
sound proof <strong>for</strong> its finding. Temporal logics used in model checking allow precise <strong>for</strong>mulation of<br />
subtle temporal properties. Model checking technology has been actively investigated and heavily<br />
invested in industry and academia. Over years new techniques such as Binary Decision Graphbased<br />
model checking [Burch et al., 1992] and SAT-based bounded model checking [Biere et al.,<br />
1999] have drastically improved the scalability of model checking technology. Today model checking<br />
has been successfully used <strong>for</strong> verifying industrial-size processor designs [Gerth, 2001] and software<br />
applications [Ball et al., 2004]. Recently probabilistic model checking gains momentum by the<br />
introduction of new symbolic techniques [Wang and Kwiatkowska, 2005]. It has been successfully<br />
used to analyze a wide range of applications such as biological processes [Kwiatkowska et al., 2008]<br />
and Randomised distributed algorithms [Kwiatkowska and Norman, 2002].<br />
In this project we will develop a <strong>for</strong>mal stochastic analysis technique derived from probabilistic<br />
model checking. Particularly we will focus on the following issues in applying probabilistic model<br />
checking to stochastic analysis of supply chains:<br />
1. Pattern-based stochastic problem <strong>for</strong>mulation. Problem <strong>for</strong>mulation is the first step towards<br />
<strong>for</strong>mal stochastic analysis of supply chains. Probabilistic model checking <strong>for</strong>mulates stochastic<br />
properties in a probabilistic temporal logics. Using temporal logics requires some knowledge in<br />
<strong>for</strong>mal methods and may pose a hurdle <strong>for</strong> the adoption of new model-checking-based analysis<br />
technique. To ease such difficulty, we propose a two-step process <strong>for</strong> problem <strong>for</strong>mulation:<br />
we will provide patterns <strong>for</strong> practitioners in supply-chain management to define stochastic<br />
properties of interests in a language closer to their background, and we will develop an<br />
algorithm that can translate a pattern-based representation of stochastic property to one in<br />
a temporal logic.<br />
12
2. Proof extraction and game-based result interpretation. Probabilistic model checking analyzes<br />
a system on a stochastic property by constructing a mathematical proof. Such proof carries<br />
rich in<strong>for</strong>mation about the system with respect to the stochastic property. For instance, in<br />
context of supply-chain analysis, a proof can be used to understand analysis result and to<br />
debug a supply-chain design. Furthermore, probabilistic model checkers like PRISM [Hinton<br />
et al., 2006] answers the maximal (or minimal) probability in which a stochastic property may<br />
hold on a supply chain. A proof can be used to <strong>for</strong>mulate an optimal solution that reaches<br />
such maximal (or minimal) probability.<br />
Current research on extracting proofs from probabilistic model checkers is still limited because<br />
of the complexity of these proofs. Existing work (cf. [Han et al., 2009]) emphasized on<br />
<strong>for</strong>mats of proofs. More research is needed on how to extract these proofs from existing<br />
model checking algorithms and interpreting them to end users. In this project, we will study<br />
the problems of proof extraction and result interpretation in context of supply chain analysis.<br />
We will extend Tan and Cleaveland [2002]’s previous works on extracting proofs from a<br />
traditional model checker to the domain of probabilistic model checking. Previously Tan<br />
[2002] also developed a generic game-theoretic framework <strong>for</strong> interpreting proofs from various<br />
automated verification procedures. The framework worked by playing an interactive game<br />
with a user. By making decisions during the game, the user could choose part of the proof<br />
(s)he wanted to explore. Game-theoretic approach is especially suitable <strong>for</strong> explaining proofs<br />
with branching logics, including proofs from <strong>for</strong>mal stochastic analysis of supply chains. Such<br />
proofs are (potentially infinite) decision trees extended with probabilities and rewards. In<br />
this project we will develop a game-theoretic approach that allows an end user to explore<br />
proofs constructed by a probabilistic model checker.<br />
5.4 Specific aim 4: implement an open source tool <strong>for</strong> knowledge dissemination<br />
and technology transfer<br />
The purpose of this research is to enable modeling and automated stochastic analysis technology<br />
that are efficient and scalable <strong>for</strong> real-world large-scale supply chains. Besides the project’s proposed<br />
technological advances, the success of the project also largely depends on how effective we can<br />
disseminate knowledge and transfer technology to other researchers and practitioners. <strong>An</strong> opensource<br />
tool will be an excellent vehicle <strong>for</strong> this purpose: it allow practitioners to try out new<br />
technology and integrate the new tool to his/her existing workflow. It also provides an open<br />
plat<strong>for</strong>m <strong>for</strong> researchers to test and extend new technology.<br />
Our team members have successfully developed a variety of open-source tools including Concurrent<br />
Workbench [Cleaveland et al., 2000], M 2 IST [Tan, 2006], and most recently Simrisk [Tan<br />
and Xu, 2009b]. We will apply these experience and skills to this project. Specifically we will build<br />
the tool on Eclipse [the Eclipse Foundation, since 2004]. Eclipse is a popular open-source software<br />
development plat<strong>for</strong>m. Its model-based design frameworks EMF and GMF reduce the time and<br />
the cost <strong>for</strong> implementing an agent-based modeling framework. Eclipse supports several code generation<br />
frameworks including JET and Acceleo that can be used <strong>for</strong> building the reconfigurable<br />
generative simulation engine. Finally Eclipse adopts an open architecture. Other researchers can<br />
integrate their own analysis algorithms to the tool.<br />
13
Q1/2010 Q2/2010 Q3/2010 Q4/2010 Q1/2011 Q2/2011 Q3/2011 Q4/2011 Q1/2012 Q2/2012 Q3/2012 Q4/2012<br />
<strong>Agent</strong>-based Stochastic Modeling<br />
Extension of Markov Decision Processes<br />
Component-based design<br />
Model reusability<br />
Support <strong>for</strong> decision optimization<br />
Re-targetable generative simulation engine<br />
Load balance based on structure<br />
in<strong>for</strong>mation<br />
Thread-scheduling optimization<br />
Formal stochastic analysis<br />
Pattern-based problem <strong>for</strong>mulation<br />
Proof extract and game-based<br />
interpretation<br />
<strong>Open</strong> source tools development<br />
Table 1: The project timeline.<br />
5.5 Project timeline<br />
Table 1 gives the project timeline. The project will be carried out in a three-year period with ef<strong>for</strong>ts<br />
of two faculty members, one Ph.D. student, and one Masters student. Table 1 lists durations and<br />
finish time of activities defined <strong>for</strong> each specific aim.<br />
5.6 Project management<br />
In this project Li Tan will lead ef<strong>for</strong>ts on developing high-per<strong>for</strong>mance reconfigurable simulation<br />
technology, automated <strong>for</strong>mal stochastic analysis technique, and the open-source tool Simrisk.<br />
Shenghan Xu will lead ef<strong>for</strong>ts on agent-based stochastic modeling and the empirical study of new<br />
technologies developed in this project. The University of Idaho and Washington State University<br />
Pullman campus are connected by a 7-mile highway. During the course of this project we will hold<br />
weekly research meeting between two groups.<br />
6 Broader impact<br />
Stochastic analysis of supply chains is important <strong>for</strong> a wide range of applications, including risk<br />
supply-chain risk analysis[Chen and Zhang, 2008], contracting [van Delft and Vial, 2004], and<br />
per<strong>for</strong>mance evaluation [Wei et al., 2007]. We expect that the success of this project will advance<br />
research on these application. Because of the practical significance of stochastic analysis<br />
in supply-chain management, the project is also expected to assist companies who are interested<br />
in streamlining their supply chains. In addition, we plan to promote knowledge dissemination<br />
and technology transfer by publications, the distribution of an open-source tool, and educational<br />
programs. Theories and methods developed in this project will be shared in research community<br />
14
via publications in leading journals and major conferences in operations management and computer<br />
science. The open-source tool will be free available <strong>for</strong> practitioners who want to try out<br />
new technologies and incorporate them to their existing workflow. Built on the leading software<br />
development framework Eclipse, the tool will also provide an open framework <strong>for</strong> researchers and<br />
engineers who want to contribute and/or integrate new stochastic analysis technologies.<br />
Part of grant will be used to support a women faculty member (Shenghan Xu) <strong>for</strong> her interdisciplinary<br />
in operations management and computer science. A significant amount of the grant<br />
will be used to support a Ph.D student and a M.S. student. They will work with PIs to develop<br />
underlying technologies. Part of this research will be built into the current and future courses<br />
on supply-chain management, mathematical modeling, software engineering, and parallel computing<br />
in Washington State University and the University of Idaho. Li Tan also leads a very active<br />
undergraduate research program on software engineering in WSU, in which students help design<br />
and develop a variety of research tools, including a preliminary implementation of Simrisk as<br />
the proof of concepts. Through the development of these research tools, undergraduate students<br />
gained value experience in software design, and they learnt skills essential to interdisciplinary study.<br />
If funded, this project will contain a significant amount of undergraduate research activities. A<br />
group of undergraduate students will work with PIs and graduate students on the design and the<br />
implementation of Simrisk.<br />
The Simrisk toolkit developed in this research will be also used in an upper-division undergraduate<br />
class in supply-chain management in the University of Idaho. Its agent-based visual model<br />
editing environment will help students develop intuition behind supply-chain risk management.<br />
The tool will also allow students to try “what-if” scenarios <strong>for</strong> different risk management strategies<br />
and learn how to optimize supply chains by balancing various factors.<br />
15
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Budget and Justification<br />
1 Salary<br />
1.1 Personnel<br />
The funding <strong>for</strong> this project will include the support <strong>for</strong> 1 Ph.D. student <strong>for</strong> 3 years and 1 masters<br />
student <strong>for</strong> one academic year (9 months). The Ph.D. student will work with PIs to conduct basic<br />
research on technologies required by the project, as defined in the research plan. The masters<br />
student will assist the implementation of the open-source supply-chain modeling and stochastic<br />
analysis tool. Training these students are also part of our ef<strong>for</strong>ts to broaden the impact of this<br />
project by disseminating knowledge via graduate education.<br />
The budget will include the summer support <strong>for</strong> Dr. Li Tan <strong>for</strong> 2.0 months per year and Dr.<br />
Shenghan Xu <strong>for</strong> 1.0 month per year. With this support, PIs can spend time necessary <strong>for</strong> advising<br />
students and conducting research as outlined in our research plan.<br />
Faculty appointments at Washington State University and at the University of Idaho are generally<br />
effective on an Academic Year (AY) beginning August 16. The summer support <strong>for</strong> Dr. Li<br />
Tan (2.0 months per year) and Dr. Shenghan Xu (1.0 month per year) during the award will be<br />
used during the summer period May 16 - August 15.<br />
Graduate Research Assistants (GRAs) and Undergraduate Research Assistants (URAs) - Costs<br />
are estimated based on calculations from the matrices issued by the Graduate School at Washington<br />
State University.<br />
• 1 - GRAs (PhD level - step 47) at $21,567.00 × 1 student = $21,567.00 (12 month appointment<br />
at 50% FTE <strong>for</strong> three years of the award).<br />
• 1 - GRAs (Master level - step 42) at $22,175.00 × 1 student = $22,175.00 (12 month appointment<br />
at 50% FTE in the third year of the award).<br />
1.2 Salary Increases<br />
<strong>Based</strong> on Washington State University’s guidelines a 4% salary increase is calculated into the PI<br />
and Graduate Research Assistants employed by Washington State University. All increases take<br />
effect July 1st.<br />
1.3 Fringe Benefits<br />
Washington State University’s fringe benefits rates, as they apply to sponsored programs are as<br />
follows: 32% <strong>for</strong> faculty and professional staff, 1.5% <strong>for</strong> graduate students.<br />
1.4 Qualified Tuition Reduction<br />
It is the policy of Washington State University to provide tuition <strong>for</strong> graduate research assistants<br />
as a partial compensation <strong>for</strong> services. It is estimated <strong>for</strong> the academic year of 2010 that the QTR<br />
will be $8,598= $8,598 based on Washington State University’s guidelines a 5% QTR increase is<br />
calculated into each additional year.<br />
1
2 Travel<br />
The support <strong>for</strong> 2 trip per year <strong>for</strong> the first year, and 3 trips per year <strong>for</strong> the second and the<br />
third years are requested. These funds will support PIs and graduate students to travel to related<br />
conferences in operations management and computer science and present findings from this research.<br />
3 Equipment and Software<br />
The requested fund includes the expense <strong>for</strong> two laptop computers, two desktop computers, and an<br />
entry-level server. Laptop and desktop computers ensure that every participant of this project has<br />
adequate personal computing equipment to complete the proposed research and development work.<br />
The entry-level server will be used to host the project <strong>for</strong> developing, maintaining, and distributing<br />
the open-source tool. The requested fund will also include necessary software packages including<br />
MPL and CPLEX (<strong>for</strong> per<strong>for</strong>mance comparison with stochastic programming), and Matlab.<br />
4 Indirect Costs<br />
Washington State University’s negotiated MTDC Indirect Cost rates with DHHD are 49.5% ”oncampus”<br />
and 26% ”off-campus”. (Note: The MTDC bases consist of total costs less equipment<br />
items in excess of $5,000 or more, minus each and any portion of a subcontractor over $25,000,<br />
qualified tuition reduction).<br />
5 Subaward<br />
A subaward will be made to the University of Idaho ($79,720 over 3 year) <strong>for</strong> the portion of the<br />
research that Dr. Shenghan Xu will conduct.<br />
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